Data Review Scientist

• Must be able to solve practical problems and deal with a variety of concrete variables in situations where limited standardization exists.\n
• Must be able to interpret a variety of instructions furnished in written, oral, diagram, or schedule form.\n
• Organizes workflow and tasks to maximize efficiency (e.g., prioritize review/writing based on client/business needs, etc.).\n
• Demonstrated ability to work with others at all levels of the organization and have basic knowledge of computers and industry-related software.\n
• Ability to work effectively under pressure to meet deadlines. \n
• Ensures compliance with company policies and SOPs as well as FDA and other applicable guidelines. \n, * BS or MS in Chemistry (or closely related discipline). Relevant background in GXP/QC-compliant pharmaceutical laboratory environment.\n
• Excellent verbal and written communication and interpersonal skills, problem-solving, and organizational skills.\n
• Demonstrated ability to work in a diverse team environment.\n
• Proficiency with Microsoft software (Word, Excel and PowerPoint).\n

Come join our QA team of experts. The Data Review Scientist conducts data review activities for data generated by GMP Analytical for validation of analytical test methods, testing of cleaning samples, raw materials, process intermediates, final APIs, and final dosage forms, characterizing/qualifying Reference Standards, and Stability results. These data reviews are accomplished by review and approval of GMP Analytical Laboratory data located in notebooks, Material Release Forms, stability reports, methods qualifications/validations and reports, etc., as well as other documents as assigned.\n \n \nResponsibilities\n \n \n \n

• Reviews chromatographic/other data and related notebooks as specified by Standard Operating Procedures (SOPs). Evaluates data to ensure compliance with analytical methods, client criteria, SOPs, and Good Manufacturing Practices (GMP).\n
• Must be able to understand analytical data (chromatographic and other) and scrutinize routine and non-routine testing results to ensure compliance, accuracy, and completeness of work based on documented data. \n